@InProceedings{cao-EtAl:2017:Long1,
  author    = {Cao, Yixin  and  Huang, Lifu  and  Ji, Heng  and  Chen, Xu  and  Li, Juanzi},
  title     = {Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1623--1633},
  abstract  = {Integrating text and knowledge into a unified semantic space has attracted
	significant research interests recently. However, the ambiguity in the common
	space remains a challenge, namely that the same mention phrase usually refers
	to various entities. In this paper, to deal with the ambiguity of entity
	mentions, we propose a novel Multi-Prototype Mention Embedding model, which
	learns multiple sense embeddings for each mention by jointly modeling words
	from textual contexts and entities derived from a knowledge base. In addition,
	we further design an efficient language model based approach to disambiguate
	each mention to a specific sense. In experiments, both qualitative and
	quantitative analysis demonstrate the high quality of the word, entity and
	multi-prototype mention embeddings. Using entity linking as a study case, we
	apply our disambiguation method as well as the multi-prototype mention
	embeddings on the benchmark dataset, and achieve state-of-the-art performance.},
  url       = {http://aclweb.org/anthology/P17-1149}
}